Alternatively, CNN features can be visualized by plotting the feature maps, that is, all activation values of the same convolutional filter.
However, feature maps only show which parts of the input the filter responds to but do not visualize the detected pattern [1]}.
To further investigate which pattern is detected by the convolutional filter, an artificial input which maximally activates this filter can be created through optimization [1]}, [3]}, [4]}.
Using a data example or random values as initial input, its values are updated by an optimization algorithm to maximize the activation values of the feature map of interest.
This approach for CNN feature visualization is also referred to as AM.
If inputs are optimized without constraints, they can appear unrealistic to a human and hence be hard to interpret.
Therefore, AM is typically performed with regularization techniques that penalize unnatural inputs [4]}.
While regularization makes optimized inputs more realistic, it can decrease the faithfulness of the pattern visualization if the optimization overemphasizes the similarity of the optimized input to natural data.
